# CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference

@inproceedings{Nygaard2022CONNECTAN, title={CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference}, author={Andreas Nygaard and Emil Brinch Holm and Steen Hannestad and Thomas Tram}, year={2022} }

Bayesian parameter inference is an essential tool in modern cosmology, and typically requires the calculation of 105–106 theoretical models for each inference of model parameters for a given dataset combination. Computing these models by solving the linearised Einstein-Boltzmann system usually takes tens of CPU core-seconds per model, making the entire process very computationally expensive. In this paper we present connect, a neural network framework emulating class computations as an easy-to…

## 2 Citations

### CosmicNet II: emulating extended cosmologies with efficient and accurate neural networks

- Physics, Computer ScienceJournal of Cosmology and Astroparticle Physics
- 2022

A more efficient set of networks that are already trained for extended cosmologies beyond ΛCDM, with massive neutrinos, extra relativistic degrees of freedom, spatial curvature, and dynamical dark energy are presented.

### Fast and robust Bayesian Inference using Gaussian Processes with GPry

- Computer Science
- 2022

The GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters is presented, based on generating a Gaussian Process surrogate model of the log-posterior aided by a Support Vector Machine classiﬁer that excludes extreme or non-ﬂnite values.

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